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A Study on Various Technologies to Solve the Routing Problem in Internet of Vehicles (IoV)

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Abstract

Internet of Vehicles (IoV) can be pivotal factor towards realization of Intelligent Transportation Systems. IoV principle focus is to have time decisive safety applications, optimize traffic flow, infotainment and Vehicular network with the intention to improve road safety through deployment of application allowing drivers to anticipate danger on the road. One of the important challenges of IoV is timely, reliable, and consistent propagation of messages among vehicles which enable drivers to take appropriate decisions to have improved road safety. Many proposals has been put forward by researchers to identify the traffic jam and routing the vehicular nodes in urban and highway roads for consistent, safe and secured driving environment. Even though the protocols have several limitations including lack of scalability to larger networks, routing overheads, etc. To overcome these limitations bio-inspired, big data, genetic algorithm, machine learning approaches have been proposed to identify and route packets among vehicular nodes in an optimized manner. The paper contains the survey of already proposed method and new approach to identify and route the vehicular node for the IoV environment.

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References

  1. ETSI EN 302 665 V1.1.1 (2010-09, European Standard (Telecommunications series) Intelligent Transport Systems (ITS); Communications Architecture.

  2. Papadimitratos, P. et. al. (2009). Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation. In IEEE communications magazine (pp. 84–95)

  3. Zheng, K. (2016). Architecture of heterogeneous vehicular networks. Berlin: Springer.

    Book  Google Scholar 

  4. Karagiannis, G. et. al. (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. In IEEE communication surveys and tutorials.

  5. Eze, E. C., Zhang, S., Liu, E., et al. (2016). Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development. International Journal of Automation and Computing, 13, 1–18. https://doi.org/10.1007/s11633-015-0913-y.

    Article  Google Scholar 

  6. Sachdev, A., Mehta, K., & Malik, L. (2016). Design of protocol for cluster based routing in VANET using Fire Fly Algorithm. In 2016 IEEE international conference on engineering and technology (ICETECH) (pp. 490–495), Coimbatore.

  7. Shivashankar, S. M., Prasad, P. R., Kumar, S. S., & Kumar, K. N. S. (2016) An efficient routing algorithm based on ant colony optimisation for VANETs. In 2016 ieee international conference on recent trends in electronics, information and communication technology (RTEICT) (pp. 436–440), Bangalore.

  8. Mohammadnia, A., Alguliyev, R., Yusifov, F., & Jamali, S. (2016). Routing algorithm for vehicular Ad Hoc network based on dynamic Ant Colony optimization. International Journal of Electronics and Electrical Engineering, 4, 79–83. https://doi.org/10.18178/ijeee.4.1.79-83.

    Article  Google Scholar 

  9. Härri, J., Filali, F., & Bonnet, C. (2019). On meaningful parameters for routing in vanets urban environments under realistic mobility patterns.

  10. Deshmukh, A. R., & Dorle, S. S. (2015). Bio-inspired optimization algorithms for improvement of vehicle routing problems. In 2015 7th international conference on emerging trends in engineering and technology (ICETET) (pp. 14–18), Kobe. https://doi.org/10.1109/ICETET.2015.27.

  11. Mehta, K., Bajaj, P. R., & Malik, L. G. (2016). Fuzzy bacterial foraging optimization zone based routing (FBFOZBR) protocol for VANET. In 2016 international conference on ICT in business industry and government (ICTBIG) (pp. 1–10), Indore.

  12. El Amine, M. F., Lakas, A., & Korichi, A. (2016). CBQoS-Vanet: Cluster-based artificial bee colony algorithm for QoS routing protocol in VANET. In 2016 international conference on selected topics in mobile and wireless networking (MoWNeT) (pp. 1–8), Cairo.

  13. Deshmukh, A. R., & Dorle, S. S. (2016). Bio-inspired optimization algorithms for improvement of vehicle routing problems. In International conference emerging trends engineering and technology ICETET (vol. 2016, pp. 14–18).

  14. Xiao, L., Hajjam-El-Hassani, A., & Dridi, M. (2017). An application of extended cuckoo search to vehicle routing problem. In 2017 international colloquium on logistics and supply chain management (LOGISTIQUA) Rabat (pp. 31–35).

  15. Kochhar, R., & Mandoria, H. (2015). Performance study of VANET using ant based routing algorithms. In 2015 2nd international conference on computing for sustainable global development (INDIACom), New Delhi (pp. 1803–1806).

  16. Medetov, S., Bakhouya, M., Gaber, J., Zinedine, K., & Wack, M. (2014). A bee-inspired approach for information dissemination in VANETs. In 2014 international conference on multimedia computing and systems (ICMCS), Marrakech (pp. 849–854).

  17. Fattahi, E., Bidar, M., & Kanan, H. R. (2014). Fuzzy Krill Herd optimization algorithm. In 2014 first international conference on networks and soft computing (ICNSC2014), Guntur (pp. 423–426).

  18. Sataraddi, M. J., Kakkasageri, M. S., Kori, G. S., & Patil, R. V. (2017). Intelligent routing for hybrid communication in VANETs. In 2017 IEEE 7th international advance computing conference (IACC) Hyderabad (pp. 385–390).

  19. Chen, C., Liu, L., Qiu, T., Member, S., Yang, K., & Member, S. (2018). ASGR : An artificial spider-web-based geographic routing in heterogeneous vehicular networks (pp. 1–17).

  20. Goudarzi, F., Asgari, H., Member, S., Al-raweshidy, H. S., & Member, S. (2018). Traffic-aware VANET routing for city environments—a protocol based on ant colony optimization (pp. 1–11).

  21. Zhang, X., & Zhang, X. (2016). A binary artificial bee colony algorithm for constructing spanning trees in vehicular ad hoc networks. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2016.07.001.

    Article  Google Scholar 

  22. Tian, D., Member, S., Zheng, K., Zhou, J., Duan, X., & Wang, Y. (2017). A microbial inspired routing protocol for VANETs (vol. 4662, No. c, pp. 1–10).

  23. Khan, M., Aadil, F., Rehman, Z., Khan, S., Shah, Dr., Azmat, P., et al. (2018). Grey wolf optimization based clustering algorithm for vehicular Ad-Hoc networks. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2018.01.002.

    Article  Google Scholar 

  24. Saritha, V., Krishna, P., Misra, S., & Obaidat, M. (2017). Learning automata based optimized multipath routing using leapfrog algorithm for VANETs (pp. 1–5). https://doi.org/10.1109/ICC.2017.7997401.

  25. Karvounas, D., Bantouna, A., Georgakopoulos, A., Tsagkaris, K., Stavroulaki, V., & Demestichas, P. (2015). A machine-learning approach based on bio-inspired intelligence. https://doi.org/10.1002/9781119057246.ch8.

  26. Chowdhary, N., & Kaur, P. (2018). Dynamic route optimization using nature-inspired algorithms in IoV. https://doi.org/10.1007/978-981-10-5828-8_47.

  27. Aadil, F., Ahsan, W., Rehman, Z., Shah, Dr., Azmat, P., Rho, S., & Mehmood, I. (2018). Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). The Journal of Supercomputing. https://doi.org/10.1007/s11227-018-2305-x.

    Article  Google Scholar 

  28. Kar, A. (2016). Bio inspired computing-a review of algorithms and scope of applications. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2016.04.018.

    Article  Google Scholar 

  29. Khan, M. F., Aadil, F., Maqsood, M., Bukhari, S. H. R., Hussain, M., & Nam, Y. (2019). Moth flame clustering algorithm for internet of vehicle (MFCA-IoV). IEEE Access, 7, 11613–11629. https://doi.org/10.1109/ACCESS.2018.2886420.

    Article  Google Scholar 

  30. Jiang, R., & Zhu, Y. (2019). Wireless access in vehicular environment. In X. Shen, X. Lin, & K. Zhang (Eds.), Encyclopedia of wireless networks. Cham: Springer.

    Google Scholar 

  31. Ali, M., Malik, A. W., Rahman, A. U., Iqbal, S., & Hamayun, M. M. (2019). Position-based emergency message dissemination for Internet of vehicles. International Journal of Distributed Sensor Networks. https://doi.org/10.1177/1550147719861585.

    Article  Google Scholar 

  32. Zhu, W., Gao, D., Foh, C., Zhang, H.-k, & Chao, H.-C. (2017). Reliable emergency message dissemination protocol for urban internet of vehicles. IET Communications. https://doi.org/10.1049/iet-com.2016.0661.

    Article  Google Scholar 

  33. Liu, Q., Kumar, S., & Mago, V. (2017). SafeRNet: Safe transportation routing in the era of internet of vehicles and mobile crowd sensing (pp. 299–304). https://doi.org/10.1109/CCNC.2017.7983123.

  34. Fan, X., Huang, C., Zhu, J., & Fu, B. (2019). Replication-based data dissemination in connected internet of vehicles. Wireless Communications and Mobile Computing, 2019, 1–16. https://doi.org/10.1155/2019/2150524.

    Article  Google Scholar 

  35. Fan, X., Huang, C., Zhu, J., & Fu, B. (2018). R-DRA: A replication-based distributed randomized algorithm for data dissemination in connected vehicular networks. Wireless Networks. https://doi.org/10.1007/s11276-018-01895-3.

    Article  Google Scholar 

  36. Song, W., Rehman, S. U., & Awan, M. B. (2015). Road aware information sharing in VANETs. KSII Transactions on Internet and Information Systems, 9(9), 3377–3395. https://doi.org/10.3837/tiis.2015.09.006.

    Article  Google Scholar 

  37. Alghushairy, O., Aljohani, W., & Alharthi, M. (2014). An efficient routing protocol for connecting vehicular networks to the internet. In IJARCCE (pp. 8359–8361). https://doi.org/10.17148/IJARCCE.2014.31101.

  38. Wu, H., Tang, H., Dong, L. (2014). A novel routing protocol based on mobile social networks and internet of vehicles (pp. 1–10). https://doi.org/10.1007/978-3-319-11167-4_1.

  39. Gandhi, A., & Jadhav, B. T. (2012). Role of wireless technology for vehicular network.

  40. Habib, S., Hannan, M. A., Javadi, M. S., Samad, S. A., Muad, A. M., & Hussain, A. (2013). Inter-vehicle wireless communications technologies, issues and challenges. Information Technology Journal, 12, 558–568.

    Article  Google Scholar 

  41. Fangchun, Y., Wang, S., Jinglin, Li., Liu, Z., & Sun, Q. (2014). An overview of internet of vehicles. Communications China, 11, 1–15. https://doi.org/10.1109/CC.2014.6969789.

    Article  Google Scholar 

  42. Song, W.-C., Rehman, S., & Awan, B. (2015). Road aware information sharing in VANETs. KSII Transactions on Internet and Information Systems, 9, 3377–3395. https://doi.org/10.3837/tiis.2015.09.006.

    Article  Google Scholar 

  43. Ang, Li., Seng, K., Ijemaru, G., & Adamu, M. (2018). Deployment of IoV for smart cities: Applications, architecture and challenges. IEEE Access, 7, 6473–6492. https://doi.org/10.1109/ACCESS.2018.2887076.

    Article  Google Scholar 

  44. Santamaria, A. F., Tropea, M., Fazio, P., Rango, De., & Floriano. . (2018). Managing emergency situations in VANET through heterogeneous technologies cooperation. Sensors, 18, 1461. https://doi.org/10.3390/s18051461.

    Article  Google Scholar 

  45. Sherazi, H., Khan, Z. A., Iqbal, R., Rizwan, S., & Imran, M., & Awan, K. (2018). A heterogeneous IoV architecture for data forwarding in vehicle to infrastructure communication. https://doi.org/10.1155/2019/3101276.

  46. Liu, Y., Cheng, D., Wang, Y., Cheng, J., & Gao, S. (2018). A novel method for predicting vehicle state in internet of vehicles. Mobile Information Systems, 2018, 1–13. https://doi.org/10.1155/2018/9728328.

    Article  Google Scholar 

  47. Xu, W., Zhou, H., Cheng, N., Lyu, F., Shi, W., Chen, J., & Shen, X. (2018). Internet of vehicles in big data era. IEEE/CAA Journal of Automatica Sinica, 5, 19–35.

    Article  Google Scholar 

  48. Russell, B., Littman, M., & Trappe, W. (2011). Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol. International Journal of Communication Systems, 24, 950–966. https://doi.org/10.1002/dac.1202.

    Article  Google Scholar 

  49. Zhao., L., Li, Y., Meng, C., Gong, C., & Tang, X. (2016). A SVM based routing scheme in VANETs. In 2016 16th international symposium on communications and information technologies (ISCIT) (pp. 380–383) Qingdao. https://doi.org/10.1109/ISCIT.2016.7751655.

  50. Mukhutdinov, D., Filchenkov, A., Shalyto, A., & Vyatkin, V. (2019). Multi-agent deep learning for simultaneous optimization for time and energy in distributed routing system. Future Generation Computer Systems, 94, 587–600. https://doi.org/10.1016/j.future.2018.12.037.

    Article  Google Scholar 

  51. Darwish, T., & Abu, B. K. (2018). Fogbased intelligenttransportationbigdataanalyticsintheinternet of vehicles environment: Motivations, architecture, challenges and critical issues. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2815989.

    Article  Google Scholar 

  52. Lyu, F., Cheng, N., Zhu, H., Zhou, H., Xu, W., Li, M., & Shen, X. (2018). Intelligent context-aware communication paradigm design for IoVs based on data analytics. IEEE Network, 32, 74–82. https://doi.org/10.1109/MNET.2018.1800067.

    Article  Google Scholar 

  53. Lifeng, Y., Liangming, C., Ningwei, W., & Zhifang, L. (2017). Routing Optimization Algorithms Based On Node Compression In Big Data Environment. Scientific Programming, 2017, 7. https://doi.org/10.1155/2017/2056501.

    Article  Google Scholar 

  54. Zheng, S. (2019). Solving vehicle routing problem: A big data analytic approach. IEEE Access., 7, 169565–169570. https://doi.org/10.1109/ACCESS.2019.2955250.

    Article  Google Scholar 

  55. Gebremeskel, G. B., Chai, Y., & Yang Z. (2014) The paradigm of big data for augmenting internet of vehicle into the intelligent cloud computing systems. In R.C.H. Hsu, S. Wang (Eds.) Internet of vehicles–technologies and services.

  56. Mohammed, S., Al-kahtani, (2016). Big data networking: requirements, architecture and issues international journal of wireless and mobile networks (IJWMN) (Vol. 8, No. 6). https://doi.org/10.5121/ijwmn.2016.8604.

  57. Maeda, O., Nakamura, M., Ombuki, B. M., & Onaga, K. (1999). A genetic algorithm approach to vehicle routing problem with time deadlines in geographical information systems. In IEEE SMC'99 conference proceedings. 1999 IEEE international conference on systems, man, and cybernetics (Cat. No.99CH37028) (Vol. 4, pp. 595–600), Tokyo, Japan. https://doi.org/10.1109/ICSMC.1999.812471.

  58. Ahn, C. W., & Ramakrishna, R. S. (2003). A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transactions on Evolutionary Computation, 6, 566–579. https://doi.org/10.1109/TEVC.2002.804323.

    Article  Google Scholar 

  59. Christy, J. J., Rekha, D., Vijayakumar, V., & Surya, P. V. B. (2019). Broadcast scheduling problem in VANETs: A discrete genetic algorithm approach. In International journal of recent technology and engineering (IJRTE) (Vol. 7, No. 6S2), ISSN: 2277–3878.

  60. Lai, W. K., Lin, M.-T., & Yang, Y.-H. (2015). A machine learning system for routing decision-making in urban vehicular Ad Hoc networks. International Journal of Distributed Sensor Networks., 2015, 1–13. https://doi.org/10.1155/2015/374391.

    Article  Google Scholar 

  61. Wu, C., Ohzahata, S., & Kato, T. (2012). Routing in VANETs: A fuzzy constraint Q-Learning approach. In 2012 IEEE global communications conference (GLOBECOM) Anaheim, CA (pp. 195–200). https://doi.org/10.1109/GLOCOM.2012.6503112

  62. Li, R., Li, F., Li, X., & Wang, Y. (2014). QGrid: Q-learning based routing protocol for vehicular ad hoc networks. In 2014 IEEE 33rd international performance computing and communications conference (IPCCC) Austin, TX (pp. 1–8). https://doi.org/10.1109/PCCC.2014.7017079

  63. Tekiner, F., & Srikanth, T. (2004). Comparison of the Q-routing and shortest path routing algorithms.

  64. Sarao, P. (2019). Machine learning and deep learning techniques on wireless networks. International Journal of Engineering Research and Technology., 12, 311–320.

    Google Scholar 

  65. Choi, S.P., & Yeung, D. (1995). Predictive Q-routing: A memory-based reinforcement learning approach to adaptive traffic control. In NIPS.

  66. Kumar, S., & Miikkulainen, R. (1999). Confidence based dual reinforcement Q-routing: An adaptive online network routing algorithm. Science, 2, 6.

    Google Scholar 

  67. Haraty, R., & Traboulsi, B. (2012). MANET with the Q-routing protocol.

  68. Hendriks, T., Camelo, M., & Latré, S. (2018). Q 2 -Routing: A Qos-aware Q-routing algorithm for wireless ad hoc networks (pp. 108–115). https://doi.org/10.1109/WiMOB.2018.8589161.

  69. Mohammadreza, N., Afshin, O. M., Takác L., & Snyder, V. (xxxx). Reinforcement learning for solving the vehicle routing problem.

  70. Sun, P., Hu, Y., Lan, J., Tian, L., & Chen, M. (2019). TIDE: Time-relevant deep reinforcement learning for routing optimization. Future Generation Computation System, 99, 401–409.

    Article  Google Scholar 

  71. Yang, J., Zhang, H., Pan, C., & Sun, W. (2013). Learning-based routing approach for direct interactions between wireless sensor network and moving vehicles. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. https://doi.org/10.1109/ITSC.2013.6728518.

    Article  Google Scholar 

  72. Boyan, J., & Littman, M. (1999). Packet routing in dynamically changing networks: A reinforcement learning approach. Advances in Neural Information Processing Systems., 6, 43.

    Google Scholar 

  73. Geyer, F., & Carle, G. (2018). Learning and generating distributed routing protocols using graph-based deep learning (pp. 40–45). https://doi.org/10.1145/3229607.3229610.

  74. Valadarsky, A., Schapira, M., Shahaf, D., & Tamar, A. (2017). A machine learning approach to routing.

  75. Stampa, G., Arias, M., Sanchez-Charles, D., Muntés-Mulero, V., & Cabellos-Aparicio, A. (2017). A Deep-Reinforcement Learning Approach For Software-Defined Networking Routing Optimization. ArXiv, abs/1709.07080.

  76. Mili, R., Chikhi, S. (2019). Reinforcement learning based routing protocols analysis for mobile ad -hoc networks. https://doi.org/10.1007/978-3-030-19945-6_17.

  77. Qi, Z., Cai, Y., & Zhou, Q. (2014). Accurate prediction of detailed routing congestion using supervised data learning. In 2014 32nd IEEE international conference on computer design, ICCD (pp. 97–103). https://doi.org/10.1109/ICCD.2014.6974668.

  78. Nawsher, K., Ibrar, Y., Ibrahim, A. T. H., Zakira, I., Waleed, K. M. A., Muhammad, A., et al. (2014). Big Data: Survey, Technologies, Opportunities, and Challenges. Hindawi: Hindawi Publishing Corporation. https://doi.org/10.1155/2014/712826.

    Book  Google Scholar 

  79. Xu, W., et al. (2018). Internet of vehicles in big data era. IEEE/CAA Journal of Automatica Sinica, 5(1), 19–35. https://doi.org/10.1109/JAS.2017.7510736.

    Article  Google Scholar 

  80. Ashraf, D. (2018). Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3(2), 231–246. https://doi.org/10.1016/j.fcij.2018.06.001.

    Article  Google Scholar 

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  1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Tanuja Kayarga & S. Ananda Kumar

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Kayarga, T., Kumar, S.A. A Study on Various Technologies to Solve the Routing Problem in Internet of Vehicles (IoV). Wireless Pers Commun 119, 459–487 (2021). https://doi.org/10.1007/s11277-021-08220-w

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